A delivery object refers here to a physical object that may be delivered from a sending party to a receiving party based on destination address information provided with the physical object. Delivery objects are typically collected from a plurality of collection points used by sending parties to one or more processing sites. In a processing site, destination address information provided with the physical object is read and the physical object is routed according to the address information in a delivery service that delivers the physical object to the destination address, or to another processing site in a route towards the destination address.
The processing applies delivery information that should be available to it while the physical object is processed. As discussed, an important piece of delivery information is the destination address. The delivery object may, however, carry also other type of delivery information that may be relevant for a party involved with the delivery service. For example, in case delivery to the receiving party fails, the delivery object may need to be returned back to the sender. For this, the delivery object may carry address of the sending party. Furthermore, the provider of the delivery service may charge its services by means of postal indicia attached, stamped, or franked on the physical objects. It may be in the interest of the service provider to monitor that the values carried by the postal indicia of a delivery object indeed match with the defined service charges.
Delivery information provided on a delivery object thus refers here to any piece of information that is provided for a delivery processing system together with the physical object such that they form in combination a delivery object. The information may be located in some surface of a physical object, or be provided in a separate object that is attached to the physical object. The delivery information may also be provided or input separately to the processing system, as long as the logical connection between the physical object and the information is available to the processing system at the time the physical object is processed.
The volumes in efficient delivery systems are significant and the devices that transfer and scan the delivery objects need to be very fast. If the delivery objects are similar and delivery information is provided consistently according to a predefined convention, a specific region of interest is very easy to detect. This is not, however, the case in very many delivery systems. For example, in mail delivery systems, the size and shape and addressing conventions of the delivered mail objects vary significantly, so determination of the region of interest for a particular mail delivery process is not at all a straightforward task. Moreover, the present region of interest detection solutions seek a balance between throughput time and accuracy. Accuracy in region of interest detection means that the information forwarded from region of interest detection indeed comprises specific information that is relevant for one or more processing tasks. By compromising accuracy, one can achieve impressive processing times in the region of interest stage, but the disturbance to subsequent processing steps from use of irrelevant information may, however, be unacceptable.
Delivery information is typically provided in form of printed characters, so use of delivery information for the processing requires optical character recognition (OCR). Most OCR solutions easily detect regions carrying characters and identifying address information from recognized characters is naturally very accurate. However, OCR is a complex task that requires a lot of computing, and does not always meet the strict time requirements of efficient delivery systems. Image data items are thus preferably preprocessed to reduce the amount of data fed into OCR and thus minimize the delay caused by OCR to the processing.
It is known that accuracy of solutions may be improved before OCR with extensive rulesets by means of which a specific region of interest may be determined before OCR. However, such rulesets are typically application-specific, and may need to incorporate methods and conventions applied by a number of delivery object sources. This means that a lot of tailoring and maintenance effort must be vested for creating and maintaining such systems. In addition, rulesets can only cover a number of possible variations, so typically accuracy of ruleset-based systems varies between 50-70%.